Libraries

In [101]:
import os 
import pandas as pd
import numpy as np
import scipy.stats as st
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px



Reading the Data

In [102]:
list_path = "Data/Oakland_PD_2019.csv"
ripa_df = pd.read_csv(list_path)
ripa_df.head(5)
Out[102]:
DOJ_RECORD_ID PERSON_NUMBER AGENCY_ORI AGENCY_NAME TIME_OF_STOP DATE_OF_STOP STOP_DURATION CLOSEST_CITY SCHOOL_CODE SCHOOL_NAME ... ROS_NONCRIMINAL_TRANSPORT ROS_CONTACT_LEGAL_GUARDIAN ROS_PSYCH_HOLD ROS_US_HOMELAND ROS_REFERRAL_SCHOOL_ADMIN ROS_REFERRAL_SCHOOL_COUNSELOR ROS_WARNING_CDS ROS_CITATION_CDS ROS_IN_FIELD_CITE_RELEASE_CDS ROS_CUSTODIAL_WOUT_WARRANT_CDS
0 S01092009115Y0G48V7P 1 CA0010900 OAKLAND PD 1500 26-JAN-19 10 OAKLAND NaN NaN ... 0 0 0 0 0 0 NaN 54011 NaN NaN
1 W7110192824A1JIBP56D 1 CA0371100 SAN DIEGO PD 1541 25-JUN-19 5 LA MESA NaN NaN ... 0 0 0 0 0 0 NaN 66205 NaN NaN
2 S010920091KTGP7LC9K9 1 CA0010900 OAKLAND PD 1616 19-JUN-19 40 OAKLAND NaN NaN ... 0 0 0 0 0 0 54566 NaN NaN 24054, 28035, 50037
3 S010920091AX0B2XG8RH 1 CA0010900 OAKLAND PD 1824 08-JAN-19 15 OAKLAND NaN NaN ... 0 0 0 0 0 0 NaN NaN NaN NaN
4 S0109200910BGJ8W1GDW 1 CA0010900 OAKLAND PD 2320 02-MAY-19 30 OAKLAND NaN NaN ... 0 0 0 0 0 0 NaN NaN NaN 22004, 22012

5 rows × 118 columns



Data Elements and Values (within our interest) as Defined by the RIPA Data Documentation


RAE_FULL:


Perceived Race or Ethnicity of Citizen. Individuals perceived as one or more races/ethnicities (columns Q-W) are counted as "Multiracial"
Values: 1 Asian, 2 Black, 3 Hispanic/Latino, 4 Middle Eastern or South Asian, 5 Native American, 6 Pacific Islander, 7 White, 8 Multiracial.

G_FULL:


Perceived gender of person stopped. Individuals perceived as one gender nonconforming and another gender (columns Z – AD) are counted as “Gender Nonconforming”
Values: 1 Male, 2 Female, 3 Transgender Man/Boy, 4 Transgender Woman/Girl, 5 Gender Nonconforming

AGE: Perceived age of person stopped.


Values: Alpha-numeric

REASON_FOR_STOP:


Reason for stop: traffic violation, reasonable suspicion, parole/brobation arrest warrant, investigation for truancy, consensual encounter
Values: 1 Traffic Violation, 2 Reasonable Suspicion, 3 Parole/Probation/PRCS/mandatory supervision, 4 Konwledge of outstanding arrest warrant/wanted person, 5 Investigation to determine weather a person was truant, 6 Consensual encounter resulting in search, 7 Possible conduct under Education Code, 8 Determine whether student violated school policy

ADS_HANDCUFFED:


Action taken by officer during stop: handcuffed or flex cuffed
Values: 0 No, 1 Yes

TPS_DRUGS:


Type of property seized: drugs/narcotics
Values: 0 No, 1 Yes

AGENCY_NAME:


Agency name
Values: Alpha-numeric



Potential Regions of Interest



TPS_ALCOHOL:


Type of property seized: alcohol
Values: 0 No, 1 Yes

TIME_OF_STOP:


Time of stop
Values: 24 hour time clock

DATE_OF_STOP:


Date of stop
Values: Date format

STOP_DURATION:


Duration of stop in minutes
Values: Minute format

PERSON_NUMBER:


A unique system-generated person identification number
Values: Alpha-numeric

CLOSEST_CITY:


Location of stop: closest city
Values: Alpha





Creating a new Dataframe with the columns of interest


• This is up to interpretation, I have defined the columns of interest as those highligted within the assesment instructions.
• I have also added some extra columns to explore for curiosity if I have time.
In [103]:
oakland_pd = ripa_df[['RAE_FULL', 'G_FULL', 'AGE', 'REASON_FOR_STOP', 'ADS_HANDCUFFED', 'TPS_DRUGS', 'TPS_ALCOHOL', 'PERSON_NUMBER', 'TIME_OF_STOP', 'DATE_OF_STOP', 'STOP_DURATION', 'CLOSEST_CITY', 'AGENCY_NAME']]
oakland_pd.head(5)
Out[103]:
RAE_FULL G_FULL AGE REASON_FOR_STOP ADS_HANDCUFFED TPS_DRUGS TPS_ALCOHOL PERSON_NUMBER TIME_OF_STOP DATE_OF_STOP STOP_DURATION CLOSEST_CITY AGENCY_NAME
0 7 1 150 1 0 NaN NaN 1 1500 26-JAN-19 10 OAKLAND OAKLAND PD
1 2 1 35 2 0 NaN NaN 1 1541 25-JUN-19 5 LA MESA SAN DIEGO PD
2 3 2 25 1 1 0.0 0.0 1 1616 19-JUN-19 40 OAKLAND OAKLAND PD
3 2 1 20 2 0 NaN NaN 1 1824 08-JAN-19 15 OAKLAND OAKLAND PD
4 7 1 40 2 1 NaN NaN 1 2320 02-MAY-19 30 OAKLAND OAKLAND PD

Selecting Only the stops by Oakland PD
In [104]:
oakland_pd = oakland_pd[oakland_pd["AGENCY_NAME"] == "OAKLAND PD"]
oakland_pd.head()
Out[104]:
RAE_FULL G_FULL AGE REASON_FOR_STOP ADS_HANDCUFFED TPS_DRUGS TPS_ALCOHOL PERSON_NUMBER TIME_OF_STOP DATE_OF_STOP STOP_DURATION CLOSEST_CITY AGENCY_NAME
0 7 1 150 1 0 NaN NaN 1 1500 26-JAN-19 10 OAKLAND OAKLAND PD
2 3 2 25 1 1 0.0 0.0 1 1616 19-JUN-19 40 OAKLAND OAKLAND PD
3 2 1 20 2 0 NaN NaN 1 1824 08-JAN-19 15 OAKLAND OAKLAND PD
4 7 1 40 2 1 NaN NaN 1 2320 02-MAY-19 30 OAKLAND OAKLAND PD
5 3 1 25 2 1 NaN NaN 1 1130 15-MAY-19 30 OAKLAND OAKLAND PD
In [105]:
oakland_pd.info()

<class 'pandas.core.frame.DataFrame'>
Index: 24395 entries, 0 to 25227
Data columns (total 13 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   RAE_FULL         24395 non-null  int64  
 1   G_FULL           24395 non-null  int64  
 2   AGE              24395 non-null  int64  
 3   REASON_FOR_STOP  24395 non-null  int64  
 4   ADS_HANDCUFFED   24395 non-null  int64  
 5   TPS_DRUGS        1908 non-null   float64
 6   TPS_ALCOHOL      1908 non-null   float64
 7   PERSON_NUMBER    24395 non-null  int64  
 8   TIME_OF_STOP     24395 non-null  int64  
 9   DATE_OF_STOP     24395 non-null  object 
 10  STOP_DURATION    24395 non-null  int64  
 11  CLOSEST_CITY     24395 non-null  object 
 12  AGENCY_NAME      24395 non-null  object 
dtypes: float64(2), int64(8), object(3)
memory usage: 2.6+ MB
In [106]:
oakland_pd.describe()
Out[106]:
RAE_FULL G_FULL AGE REASON_FOR_STOP ADS_HANDCUFFED TPS_DRUGS TPS_ALCOHOL PERSON_NUMBER TIME_OF_STOP STOP_DURATION
count 24395.000000 24395.000000 24395.000000 24395.000000 24395.000000 1908.000000 1908.000000 24395.000000 24395.000000 24395.000000
mean 3.018528 1.347325 33.702890 2.036278 0.394097 0.312369 0.009434 1.214224 1352.095429 31.992949
std 1.801742 2.303632 14.517176 15.817108 0.488666 0.463581 0.096695 0.669571 624.952854 42.382828
min 1.000000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000
25% 2.000000 1.000000 25.000000 1.000000 0.000000 0.000000 0.000000 1.000000 932.000000 10.000000
50% 2.000000 1.000000 30.000000 2.000000 0.000000 0.000000 0.000000 1.000000 1437.000000 20.000000
75% 3.000000 2.000000 40.000000 2.000000 1.000000 1.000000 0.000000 1.000000 1831.000000 40.000000
max 8.000000 99.000000 150.000000 999.000000 1.000000 1.000000 1.000000 10.000000 2359.000000 999.000000



Mapping the Values into dictionaries

In [107]:
gender_map = {1 : "Male", 2 : "Female", 3 : "T Male", 4 : "T Female", 5 : "Nonconforming"}
race_map = {1 : "Asian", 2 : "Black", 3 : "Hispanic/Latino", 4 : "Middle Eastern or South Asian", 5 : "Native American", 6 : "Pacific Islander", 7 : "White", 8 : "Multiracial"}
reason_map = {1 : "Traffic Violation", 2 : "Reasonable Suspicion", 3 : "Parole/Probation/PRCS/mandatory supervision", 4 : "Knowledge of outstanding arrest warrant/wanted person", 5 : "Investigation to determine weather a person was truant", 6 : "Consensual encounter resulting in search", 7 : "Possible conduct under Education Code", 8 : "Determine whether student violated school policy"}
handcuffed_map = {0 : "No", 1 : "Yes"}
drugs_map = {0 : "No", 1 : "Yes"}
alc_map = {0 : "No", 1 : "Yes"}
In [108]:
race_counts = oakland_pd["RAE_FULL"].replace(race_map).value_counts()
race_counts
Out[108]:
RAE_FULL
Black                            12856
Hispanic/Latino                   6031
White                             3268
Asian                             1173
Middle Eastern or South Asian      578
Multiracial                        283
Pacific Islander                   177
Native American                     29
Name: count, dtype: int64
In [109]:
gender_counts = oakland_pd["G_FULL"].replace(gender_map).value_counts()
gender_counts
Out[109]:
G_FULL
Male             17282
Female            7054
Nonconforming       20
99                  13
T Male              13
T Female            13
Name: count, dtype: int64
In [110]:
oakland_pd["AGE"].describe()
Out[110]:
count    24395.000000
mean        33.702890
std         14.517176
min          1.000000
25%         25.000000
50%         30.000000
75%         40.000000
max        150.000000
Name: AGE, dtype: float64
In [111]:
len(oakland_pd[oakland_pd["AGE"] >= 100])
Out[111]:
106



Quick Stats

In [112]:
top_3_races = race_counts.head(3)
total_top_race = top_3_races.sum()
race_ratios = top_3_races / total_top_race

race_ratios
Out[112]:
RAE_FULL
Black              0.580275
Hispanic/Latino    0.272218
White              0.147506
Name: count, dtype: float64
In [113]:
top_3_genders = gender_counts.head(3)
total_top_genders = top_3_genders.sum()
gender_ratios = top_3_genders / total_top_genders

gender_ratios
Out[113]:
G_FULL
Male             0.709558
Female           0.289621
Nonconforming    0.000821
Name: count, dtype: float64
In [114]:
oakland_pd['AGE'].mode().iloc[0]
Out[114]:
30
In [115]:
percentage_handcuffs = (oakland_pd["ADS_HANDCUFFED"].sum() / oakland_pd.shape[0])
percentage_handcuffs
Out[115]:
0.39409715105554416
In [116]:
percentage_drugs = (oakland_pd["TPS_DRUGS"].sum() / oakland_pd.shape[0])
percentage_drugs
Out[116]:
0.024431235908997744

• It seems that the most common race involved in stoppings is Black,
doubling the rate at which the following race (Hispanics/Latinos) is stopped,
while the latter almost doubles the rate of the following race (White).

• The most common Gender is Male, at about 2.4 times more than women.
• The most common age in the stops is around 30 years old.
• About 39% of the stops lead to a handcuff
• In only 2.4% of the stops are Drugs found

Irregularities


1.- Gender Value 99
2.- Reasons 99 and 999
3.- People older than 100 years





Exploring in Detail



In [117]:
oakland_pd.head()
Out[117]:
RAE_FULL G_FULL AGE REASON_FOR_STOP ADS_HANDCUFFED TPS_DRUGS TPS_ALCOHOL PERSON_NUMBER TIME_OF_STOP DATE_OF_STOP STOP_DURATION CLOSEST_CITY AGENCY_NAME
0 7 1 150 1 0 NaN NaN 1 1500 26-JAN-19 10 OAKLAND OAKLAND PD
2 3 2 25 1 1 0.0 0.0 1 1616 19-JUN-19 40 OAKLAND OAKLAND PD
3 2 1 20 2 0 NaN NaN 1 1824 08-JAN-19 15 OAKLAND OAKLAND PD
4 7 1 40 2 1 NaN NaN 1 2320 02-MAY-19 30 OAKLAND OAKLAND PD
5 3 1 25 2 1 NaN NaN 1 1130 15-MAY-19 30 OAKLAND OAKLAND PD



Assuming Missing Values as Zeroes


In [118]:
oakland_pd = oakland_pd.fillna(0)
oakland_pd.head()
Out[118]:
RAE_FULL G_FULL AGE REASON_FOR_STOP ADS_HANDCUFFED TPS_DRUGS TPS_ALCOHOL PERSON_NUMBER TIME_OF_STOP DATE_OF_STOP STOP_DURATION CLOSEST_CITY AGENCY_NAME
0 7 1 150 1 0 0.0 0.0 1 1500 26-JAN-19 10 OAKLAND OAKLAND PD
2 3 2 25 1 1 0.0 0.0 1 1616 19-JUN-19 40 OAKLAND OAKLAND PD
3 2 1 20 2 0 0.0 0.0 1 1824 08-JAN-19 15 OAKLAND OAKLAND PD
4 7 1 40 2 1 0.0 0.0 1 2320 02-MAY-19 30 OAKLAND OAKLAND PD
5 3 1 25 2 1 0.0 0.0 1 1130 15-MAY-19 30 OAKLAND OAKLAND PD



Renaming the columns for accesibility


In [119]:
oakland_pd = oakland_pd.rename(columns={"RAE_FULL" : "Race", "G_FULL" : "Gender", "AGE" : "Age", "REASON_FOR_STOP" : "Reason", "ADS_HANDCUFFED" : "Handcuffed", "TPS_DRUGS" : "Drugs", "TPS_ALCOHOL" : "Alcohol", "PERSON_NUMBER" : "Persons", "TIME_OF_STOP" : "Hour", "DATE_OF_STOP" : "Day", "STOP_DURATION" : "Duration", "CLOSEST_CITY" : "City", "AGENCY_NAME" : "Agency"})
oakland_pd.head()
Out[119]:
Race Gender Age Reason Handcuffed Drugs Alcohol Persons Hour Day Duration City Agency
0 7 1 150 1 0 0.0 0.0 1 1500 26-JAN-19 10 OAKLAND OAKLAND PD
2 3 2 25 1 1 0.0 0.0 1 1616 19-JUN-19 40 OAKLAND OAKLAND PD
3 2 1 20 2 0 0.0 0.0 1 1824 08-JAN-19 15 OAKLAND OAKLAND PD
4 7 1 40 2 1 0.0 0.0 1 2320 02-MAY-19 30 OAKLAND OAKLAND PD
5 3 1 25 2 1 0.0 0.0 1 1130 15-MAY-19 30 OAKLAND OAKLAND PD



Replacing Values as Defined in the documentation

In [120]:
oakland_pd["Gender"] = oakland_pd["Gender"].replace(gender_map)
oakland_pd["Race"] = oakland_pd["Race"].replace(race_map)
oakland_pd["Reason"] = oakland_pd["Reason"].replace(reason_map)
oakland_pd["Handcuffed"] = oakland_pd["Handcuffed"].replace(handcuffed_map)
oakland_pd["Drugs"] = oakland_pd["Drugs"].replace(drugs_map)
oakland_pd['Day'] = pd.to_datetime(oakland_pd['Day'])
oakland_pd["Alcohol"] = oakland_pd["Alcohol"].replace(alc_map)
oakland_pd.head()

/var/folders/tj/g_7k1_0n1ql5y1bqbkbsz7x00000gn/T/ipykernel_20505/2686835809.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
  oakland_pd['Day'] = pd.to_datetime(oakland_pd['Day'])
Out[120]:
Race Gender Age Reason Handcuffed Drugs Alcohol Persons Hour Day Duration City Agency
0 White Male 150 Traffic Violation No No No 1 1500 2019-01-26 10 OAKLAND OAKLAND PD
2 Hispanic/Latino Female 25 Traffic Violation Yes No No 1 1616 2019-06-19 40 OAKLAND OAKLAND PD
3 Black Male 20 Reasonable Suspicion No No No 1 1824 2019-01-08 15 OAKLAND OAKLAND PD
4 White Male 40 Reasonable Suspicion Yes No No 1 2320 2019-05-02 30 OAKLAND OAKLAND PD
5 Hispanic/Latino Male 25 Reasonable Suspicion Yes No No 1 1130 2019-05-15 30 OAKLAND OAKLAND PD





Analysis



Distribution of Age by Race

In [121]:
def age_distribution_by_race(dataframe):
    age_by_race = dataframe.groupby("Race")["Age"].describe()
    return age_by_race

age_distribution_by_race_df = age_distribution_by_race(oakland_pd)

age_distribution_by_race_df
Out[121]:
count mean std min 25% 50% 75% max
Race
Asian 1173.0 37.091219 14.043972 1.0 25.0 35.0 50.0 90.0
Black 12856.0 33.587663 12.614733 1.0 25.0 30.0 40.0 93.0
Hispanic/Latino 6031.0 29.721771 10.219881 1.0 20.0 30.0 35.0 80.0
Middle Eastern or South Asian 578.0 32.396194 10.569262 10.0 25.0 30.0 40.0 70.0
Multiracial 283.0 32.805654 14.890417 10.0 25.0 30.0 40.0 150.0
Native American 29.0 42.448276 13.149107 20.0 30.0 45.0 50.0 70.0
Pacific Islander 177.0 30.372881 9.808794 12.0 21.0 30.0 35.0 70.0
White 3268.0 40.698592 23.544773 1.0 30.0 35.0 50.0 150.0



Curious about the Max age

In [122]:
oakland_pd[(oakland_pd['Age'] > 100)].head()
Out[122]:
Race Gender Age Reason Handcuffed Drugs Alcohol Persons Hour Day Duration City Agency
0 White Male 150 Traffic Violation No No No 1 1500 2019-01-26 10 OAKLAND OAKLAND PD
165 White Male 150 Reasonable Suspicion Yes No No 1 704 2019-02-08 15 OAKLAND OAKLAND PD
184 White Male 150 Reasonable Suspicion Yes No No 1 1021 2019-06-10 40 OAKLAND OAKLAND PD
485 White Female 150 Traffic Violation No No No 1 1119 2019-11-20 15 OAKLAND OAKLAND PD
575 White Male 150 Traffic Violation No No No 1 1023 2019-06-13 10 OAKLAND OAKLAND PD
In [123]:
oakland_pd[(oakland_pd['Age'] > 100)]["Race"].value_counts()
Out[123]:
Race
White          104
Multiracial      2
Name: count, dtype: int64

* So it seems to be that in most of the instances where the age is higher than 100, the race is predominantly white.

* I will filter out these instances



Age Distribution by Race, Visualized

In [124]:
dabr = sns.violinplot(data=oakland_pd[(oakland_pd['Age'] < 100)].sort_values("Race"),
                    y="Race", x="Age",
                    saturation=0.7, palette="Set3")
dabr.set_title("Distribution of Age by Race");



Number of Stops Distributed by Race

In [125]:
sns.countplot(data=oakland_pd, y="Race", palette="pastel")
plt.title("Stop Distribution by Race")
plt.show()



Number of Stops across Races, Distributed by Age

In [126]:
sns.displot(data=oakland_pd[(oakland_pd['Age'] < 100)], x = "Age", hue = "Race", bins = 15, palette="pastel", height = 5, aspect = 2, kde = True)
plt.title("Age Distribution by Race of Stops by Oakland PD")
plt.xlabel("Age")
plt.ylabel("Number of Stops")
plt.show()



Number of Stops across Gender, Distributed by Age


I have filtered out the 99 value
In [127]:
sns.displot(data=oakland_pd[(oakland_pd['Gender'] != 99) & (oakland_pd['Age'] < 100)], x = "Age", hue = "Gender", bins = 15, palette="pastel", height = 5, aspect = 2, kde = True)
plt.title("Age Distribution by Gender of Stops by Oakland PD")
plt.xlabel("Age")
plt.ylabel("Number of Stops")
plt.show()



Percentage of Stops by Race and Gender

In [128]:
def percentage_of_stops_by_race_and_gender(dataframe):
    total_stops = len(dataframe)
    race_gender_counts = dataframe.groupby(["Race", "Gender"]).size()
    percentage_stops_by_race_gender = (race_gender_counts / total_stops) * 100
    percentage_stops_by_race_gender_df = percentage_stops_by_race_gender.reset_index(name='Percentage of Stops')
    return percentage_stops_by_race_gender_df

stops_by_race_gender_percentage_df = percentage_of_stops_by_race_and_gender(oakland_pd)

table = pd.pivot_table(stops_by_race_gender_percentage_df, index='Race', columns='Gender', values='Percentage of Stops')

table.fillna(0)
Out[128]:
Gender 99 Female Male Nonconforming T Female T Male
Race
Asian 0.000000 1.598688 3.205575 0.000000 0.004099 0.000000
Black 0.032794 16.109859 36.474687 0.028694 0.024595 0.028694
Hispanic/Latino 0.004099 5.386350 19.294937 0.020496 0.012298 0.004099
Middle Eastern or South Asian 0.000000 0.336134 2.029104 0.004099 0.000000 0.000000
Multiracial 0.000000 0.373027 0.774749 0.008198 0.004099 0.000000
Native American 0.000000 0.045091 0.073786 0.000000 0.000000 0.000000
Pacific Islander 0.000000 0.291043 0.434515 0.000000 0.000000 0.000000
White 0.016397 4.775569 8.555032 0.020496 0.008198 0.020496

Each cell represents percentages of Gender/Race combinations for all Stops

• As the values besides Male and Female are significantly low, I will group them on a single category





Filtering by Sex

In [129]:
by_sex = oakland_pd.replace({"Nonconforming": "Nonconforming/ T Male/ T Female", 
                             'T Male' : "Nonconforming/ T Male/ T Female", 
                             'T Female' :"Nonconforming/ T Male/ T Female"})[oakland_pd["Gender"] != 99]



Gender Differences in Stops, Across Races, Distributed by Age

In [130]:
sns.displot(data=by_sex, x="Age", hue="Race", col="Gender", kind="hist", bins=10, kde=True, palette="pastel", stat="density"
, height = 4, aspect = 1)
plt.xlabel("Age")
plt.show()

• Even while grouping Genders aside from Male and Female, their distributions are hard to visualize.


• I will filter these out in visualizations from now and on



Reason of Stop



Reason Counts

In [131]:
oakland_pd["Reason"].value_counts()
Out[131]:
Reason
Reasonable Suspicion                                      13578
Traffic Violation                                          9362
Knowledge of outstanding arrest warrant/wanted person      1002
Parole/Probation/PRCS/mandatory supervision                 235
Consensual encounter resulting in search                    166
Investigation to determine weather a person was truant       32
99                                                           13
999                                                           6
Possible conduct under Education Code                         1
Name: count, dtype: int64



Counts visualized

In [132]:
sns.histplot(data=oakland_pd[(oakland_pd['Reason'] != 99) & (oakland_pd['Reason'] != 999)], y="Reason", hue="Reason", palette="pastel")
plt.title("Reason for Stop")
plt.ylabel("Reason")
plt.xlabel("Number of Stops")
plt.show()

It seems to be the case that the most common reason for a stop is Reasonable suspicion, followed by Traffic violations.



For consistency in the hue display for the coming plots

In [133]:
reason_order = ["Traffic Violation", "Reasonable Suspicion", 
                "Parole/Probation/PRCS/mandatory supervision", 
                "Konwledge of outstanding arrest warrant/wanted person", 
                "Investigation to determine weather a person was truant", 
                "Consensual encounter resulting in search", 
                "Possible conduct under Education Code", 
                "Determine whether student violated school policy"]



Reason of Stops Percentaged by Race

In [134]:
def percentage_of_stops_by_reason_and_race(dataframe):
    race_reason_counts = dataframe[(dataframe["Reason"] != 99) & (dataframe["Reason"] != 999)].groupby(["Race", "Reason"]).size().unstack().fillna(0)
    race_reason_percentage = race_reason_counts.div(race_reason_counts.sum(axis=0), axis=1) * 100
    race_reason_percentage.reset_index(inplace=True)
    race_reason_percentage.rename_axis(None, axis=1, inplace=True)
    return race_reason_percentage

stops_by_reason_race_percentage_df = percentage_of_stops_by_reason_and_race(oakland_pd)

stops_by_reason_race_percentage_df
Out[134]:
Race Consensual encounter resulting in search Investigation to determine weather a person was truant Knowledge of outstanding arrest warrant/wanted person Parole/Probation/PRCS/mandatory supervision Possible conduct under Education Code Reasonable Suspicion Traffic Violation
0 Asian 3.012048 6.250 3.792415 5.957447 0.0 3.468847 6.857509
1 Black 52.409639 50.000 65.768463 65.531915 0.0 55.965532 46.250801
2 Hispanic/Latino 23.493976 34.375 19.760479 19.574468 100.0 23.022536 27.846614
3 Middle Eastern or South Asian 1.204819 0.000 1.097804 1.276596 0.0 1.701281 3.524888
4 Multiracial 0.602410 0.000 1.297405 2.127660 0.0 0.780675 1.687674
5 Native American 0.000000 0.000 0.499002 0.000000 0.0 0.147297 0.042726
6 Pacific Islander 1.204819 0.000 1.197605 0.000000 0.0 0.795404 0.587481
7 White 18.072289 9.375 6.586826 5.531915 0.0 14.118427 13.202307



Each cell represents the percentages of a Race/Reason for stop combination for all Stops

The displayed ammount represents the general proportion of a Race by a given Reason of Stop





Reason of Stop Percentaged within Race

In [135]:
def percentage_of_reason_by_race(dataframe):
    race_reason_counts = dataframe[(dataframe["Reason"] != 99) & (dataframe["Reason"] != 999)].groupby(["Race", "Reason"]).size().unstack().fillna(0)
    race_reason_percentage = race_reason_counts.div(race_reason_counts.sum(axis=1), axis=0) * 100
    race_reason_percentage.reset_index(inplace=True)
    race_reason_percentage.rename_axis(None, axis=1, inplace=True)
    return race_reason_percentage

reason_by_race_percentage_df = percentage_of_reason_by_race(oakland_pd)

reason_by_race_percentage_df
Out[135]:
Race Consensual encounter resulting in search Investigation to determine weather a person was truant Knowledge of outstanding arrest warrant/wanted person Parole/Probation/PRCS/mandatory supervision Possible conduct under Education Code Reasonable Suspicion Traffic Violation
0 Asian 0.426621 0.170648 3.242321 1.194539 0.000000 40.187713 54.778157
1 Black 0.677306 0.124562 5.130401 1.198910 0.000000 59.159206 33.709615
2 Hispanic/Latino 0.646981 0.182482 3.284672 0.763106 0.016589 51.857996 43.248175
3 Middle Eastern or South Asian 0.346620 0.000000 1.906412 0.519931 0.000000 40.034662 57.192374
4 Multiracial 0.353357 0.000000 4.593640 1.766784 0.000000 37.455830 55.830389
5 Native American 0.000000 0.000000 17.241379 0.000000 0.000000 68.965517 13.793103
6 Pacific Islander 1.129944 0.000000 6.779661 0.000000 0.000000 61.016949 31.073446
7 White 0.918836 0.091884 2.021440 0.398162 0.000000 58.713629 37.856049



Each cell represents the percentages of a Race/Reason for stop combination for all Stops of the same Race

The displayed ammount represents the relative proportion of a Race within, by a given Reason of Stop

Notes


Asian:


The most common reasons for police stops among Asians are "Reasonable Suspicion" (40.19%) and "Traffic Violation" (54.78%).

**Black:**
The most common reasons for police stops among Blacks are "Reasonable Suspicion" (59.16%) and "Traffic Violation" (33.71%).

**Hispanic/Latino:**
The most common reasons for police stops among Hispanics/Latinos are "Reasonable Suspicion" (51.86%) and "Traffic Violation" (43.25%).

**Middle Eastern or South Asian:**
The most common reasons for police stops among individuals from Middle Eastern or South Asian backgrounds are "Reasonable Suspicion" (40.03%) and "Traffic Violation" (57.19%).

**Multiracial:**
The most common reasons for police stops among Multiracial individuals are "Reasonable Suspicion" (37.46%) and "Traffic Violation" (55.83%).

**Native American:**
The most common reasons for police stops among Native Americans are "Reasonable Suspicion" (68.97%) and "Knowledge of outstanding arrest warrant/wanted person" (17.24%).

**Pacific Islander:**
The most common reasons for police stops among Pacific Islanders are "Reasonable Suspicion" (61.02%) and "Traffic Violation" (31.07%).

**White:**
The most common reasons for police stops among Whites are "Reasonable Suspicion" (58.71%) and "Traffic Violation" (37.86%).







For Race, By Sex

In [136]:
def plot_distribution_by_race(dataframe, race):
    race_data = dataframe[(dataframe["Race"] == race) & (dataframe['Age'] <= 100)]
    
    g = sns.displot(data=race_data, x="Age", hue="Reason", hue_order=reason_order, col="Gender", 
                    col_order=["Female","Male"], kind="hist", bins=10, kde=True, 
                    palette="pastel", height=5, aspect=1)
    g.fig.suptitle(f"Age Distribution for race: {race}", y=1.05)
    plt.xlabel("Age")
    plt.show()

unique_races = by_sex["Race"].unique()
for race in unique_races:
    plot_distribution_by_race(by_sex, race)



Handcuffings



Counts

In [137]:
oakland_pd["Handcuffed"].value_counts()
Out[137]:
Handcuffed
No     14781
Yes     9614
Name: count, dtype: int64



Defining the instances where a Handcuff Ocurred

In [138]:
handcuffed = oakland_pd[oakland_pd["Handcuffed"] == "Yes"]



Handcuffing Percentages by Race

In [139]:
def race_percentages_in_handcuff_stops(dataframe):
    handcuff_stops = dataframe[dataframe["Handcuffed"] == "Yes"]
    race_counts_in_handcuff_stops = handcuff_stops["Race"].value_counts()
    total_handcuff_stops = len(handcuff_stops)
    percentage_race_in_handcuff_stops = (race_counts_in_handcuff_stops / total_handcuff_stops) * 100
    percentage_race_in_handcuff_stops_df = percentage_race_in_handcuff_stops.reset_index()
    percentage_race_in_handcuff_stops_df.columns = ["Race", "Percentage in Handcuff Stops"]
    return percentage_race_in_handcuff_stops_df

race_percentages_in_handcuff_stops_df = race_percentages_in_handcuff_stops(oakland_pd)

race_percentages_in_handcuff_stops_df
Out[139]:
Race Percentage in Handcuff Stops
0 Black 58.612440
1 Hispanic/Latino 24.869981
2 White 10.328687
3 Asian 3.151654
4 Middle Eastern or South Asian 1.341793
5 Pacific Islander 0.821718
6 Multiracial 0.748908
7 Native American 0.124818

• It seems that the handcuffings of Black individuals make up almost 60% of all handcuffings.


• The following race is Hispanic/Latino making up for almost 25% of the handcuffings.
• White makes about 10% of the total handcuffs



Handcuff Trends by Reason of Stop

In [140]:
sns.histplot(data=handcuffed[(handcuffed['Reason'] != 99) & (handcuffed['Reason'] != 999)], y="Reason", hue="Reason", palette="pastel")
plt.title("Reason for Stop in Handcuffings")
plt.ylabel("Reason")
plt.xlabel("Number of Stops")
plt.show()

• Seems to be that the most common Reason for Stop that leads into a Handcuff is "Reasonable Suspicion""



Trends by Race

Distribution for the Reason of Stop in Handcuffs. For Race, by Sex

In [141]:
for race in unique_races:
    plot_distribution_by_race(handcuffed[(handcuffed['Age'] <= 100)], race)



Not Handcuffed

In [142]:
not_handcuffed = oakland_pd[oakland_pd["Handcuffed"] == "No"]
In [143]:
for race in unique_races: 
    plot_distribution_by_race(not_handcuffed[(not_handcuffed['Age'] <= 100)], race)

Note that the plotter function displays counts and not density as the stat





Drugs



Defining when were Drugs Found

In [144]:
drugs_found = oakland_pd[oakland_pd["Drugs"] == "Yes"]



Counts By Race and Sex

In [145]:
sns.displot(data=drugs_found, y="Race", palette="pastel", hue="Race", hue_order=unique_races, col="Gender", col_order=["Female","Male"])
plt.title(f"Distribution of Drugs found by Race and Sex", y=1.05)
plt.show()



Fixed



Gender Percentages Across Race

In [159]:
def percentage_of_drug_stops_by_race_and_gender(dataframe):
    valid_gender_df = dataframe[dataframe["Gender"] != 99]
    total_drug_stops = valid_gender_df["Drugs"].value_counts()["Yes"]
    race_gender_counts = valid_gender_df.groupby(["Race", "Gender"])["Drugs"].apply(lambda x: (x == "Yes").sum())
    percentage_drug_stops_by_race_gender = race_gender_counts / total_drug_stops * 100
    percentage_drug_stops_by_race_gender_df = percentage_drug_stops_by_race_gender.reset_index(
        name='Percentage in Drug Stops')
    return percentage_drug_stops_by_race_gender_df

drug_stops_by_race_gender_percentage_df = percentage_of_drug_stops_by_race_and_gender(oakland_pd)
table_drugs = pd.pivot_table(drug_stops_by_race_gender_percentage_df, index='Race', 
                             columns='Gender', values='Percentage in Drug Stops')
table_drugs.fillna(0)
Out[159]:
Gender Female Male Nonconforming T Female T Male
Race
Asian 0.335570 2.684564 0.0 0.0 0.0
Black 7.885906 52.348993 0.0 0.0 0.0
Hispanic/Latino 2.013423 22.986577 0.0 0.0 0.0
Middle Eastern or South Asian 0.000000 1.006711 0.0 0.0 0.0
Multiracial 0.503356 0.167785 0.0 0.0 0.0
Native American 0.000000 0.167785 0.0 0.0 0.0
Pacific Islander 0.000000 0.000000 0.0 0.0 0.0
White 2.013423 7.885906 0.0 0.0 0.0
In [158]:
oakland_pd["Drugs"].value_counts()["Yes"] / len(oakland_pd) * 100
Out[158]:
2.4431235908997744



Each cell represents the percentages of a Race/Gender combination for all Stops where Drugs were found within the same Race

The displayed ammount represents the relative proportion of a Race within, by Gender

The proportions are relatively low for some gender identities, suggesting that certain gender identities may be underrepresented and that there might be imbalances in the data collection process during police stops



Still, we can observe the following:

Black Males represent approximately 14.93% of all police stops.


Black Females represent approximately 6.59% of all police stops.

Hispanic/Latino Males represent approximately 7.90% of all police stops
Hispanic/Latino Females represent approximately 2.20% of all police stops

White Males represent approximately 3.50% of all police stops
White Females represent approximately 1.95% of all police stops

• Black Females get Stopped almost as twice as White Males



By Reason of Stop, Race and Sex. Distributed by Age

In [149]:
for race in unique_races:
    plot_distribution_by_race(drugs_found, race)

/var/folders/tj/g_7k1_0n1ql5y1bqbkbsz7x00000gn/T/ipykernel_20505/2764074444.py:4: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.
  g = sns.displot(data=race_data, x="Age", hue="Reason", hue_order=reason_order, col="Gender",

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[149], line 2
      1 for race in unique_races:
----> 2     plot_distribution_by_race(drugs_found, race)

Cell In[136], line 4, in plot_distribution_by_race(dataframe, race)
      1 def plot_distribution_by_race(dataframe, race):
      2     race_data = dataframe[(dataframe["Race"] == race) & (dataframe['Age'] <= 100)]
----> 4     g = sns.displot(data=race_data, x="Age", hue="Reason", hue_order=reason_order, col="Gender", 
      5                     col_order=["Female","Male"], kind="hist", bins=10, kde=True, 
      6                     palette="pastel", height=5, aspect=1)
      7     g.fig.suptitle(f"Age Distribution for race: {race}", y=1.05)
      8     plt.xlabel("Age")

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:2210, in displot(data, x, y, hue, row, col, weights, kind, rug, rug_kws, log_scale, legend, palette, hue_order, hue_norm, color, col_wrap, row_order, col_order, height, aspect, facet_kws, **kwargs)
   2207 if p.univariate:
   2209     _assign_default_kwargs(hist_kws, p.plot_univariate_histogram, histplot)
-> 2210     p.plot_univariate_histogram(**hist_kws)
   2212 else:
   2214     _assign_default_kwargs(hist_kws, p.plot_bivariate_histogram, histplot)

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:451, in _DistributionPlotter.plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)
    449     kde_kws["cumulative"] = estimate_kws["cumulative"]
    450     log_scale = self._log_scaled(self.data_variable)
--> 451     densities = self._compute_univariate_density(
    452         self.data_variable,
    453         common_norm,
    454         common_bins,
    455         kde_kws,
    456         log_scale,
    457         warn_singular=False,
    458     )
    460 # First pass through the data to compute the histograms
    461 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
    462 
    463     # Prepare the relevant data

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/distributions.py:317, in _DistributionPlotter._compute_univariate_density(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular)
    315     if common_grid:
    316         all_observations = self.comp_data.dropna()
--> 317         estimator.define_support(all_observations[data_variable])
    318 else:
    319     common_norm = False

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/_statistics.py:127, in KDE.define_support(self, x1, x2, weights, cache)
    125 """Create the evaluation grid for a given data set."""
    126 if x2 is None:
--> 127     support = self._define_support_univariate(x1, weights)
    128 else:
    129     support = self._define_support_bivariate(x1, x2, weights)

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/_statistics.py:99, in KDE._define_support_univariate(self, x, weights)
     97 def _define_support_univariate(self, x, weights):
     98     """Create a 1D grid of evaluation points."""
---> 99     kde = self._fit(x, weights)
    100     bw = np.sqrt(kde.covariance.squeeze())
    101     grid = self._define_support_grid(
    102         x, bw, self.cut, self.clip, self.gridsize
    103     )

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/seaborn/_statistics.py:142, in KDE._fit(self, fit_data, weights)
    139 if weights is not None:
    140     fit_kws["weights"] = weights
--> 142 kde = gaussian_kde(fit_data, **fit_kws)
    143 kde.set_bandwidth(kde.factor * self.bw_adjust)
    145 return kde

File /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/scipy/stats/_kde.py:199, in gaussian_kde.__init__(self, dataset, bw_method, weights)
    197 self.dataset = atleast_2d(asarray(dataset))
    198 if not self.dataset.size > 1:
--> 199     raise ValueError("`dataset` input should have multiple elements.")
    201 self.d, self.n = self.dataset.shape
    203 if weights is not None:

ValueError: `dataset` input should have multiple elements.

• Not enough Data for Middle Eastern, Pacific Islander and Native American



Handcuffs when Drugs were found

In [155]:
sns.countplot(data=drugs_found, y="Handcuffed", hue="Race", hue_order=unique_races, palette="pastel")
plt.title("Distribution of Drugs Found by Race")
plt.xlabel("Number of Stops")
plt.show()



Summarized



In [156]:
def general_stats(dataframe):
    total_handcuffed_counts = len(dataframe[dataframe["Handcuffed"] == "Yes"])

    total_drugs_found_counts = len(dataframe[dataframe["Drugs"] == "Yes"])

    total_handcuffed_counts = len(dataframe[dataframe["Handcuffed"] == "Yes"])
    
    unique_races = dataframe["Race"].unique()

    
    race_list = []
    handcuffed_percentage_list = []
    handcuffed_count_list = []
    total_stops_count_list = []
    total_stops_percentage_list = []
    relative_handcuffed_percentage_list = []
    drugs_found_count_list = []
    drugs_found_percentage_list = []
    drugs_handcuff_relation_list = []
    total_drug_percentage_list = []

    
    for race in unique_races:
        race_data = dataframe[dataframe["Race"] == race]
        race_counts = len(race_data)
        handcuffed_counts = len(race_data[race_data["Handcuffed"] == "Yes"])

        handcuffed_percentage = (handcuffed_counts / total_handcuffed_counts) * 100

        total_stops_percentage = (race_counts / len(dataframe)) * 100

        relative_handcuffed_percentage = (handcuffed_counts / race_counts) * 100
        
        drugs_found_counts = len(race_data[race_data["Drugs"] == "Yes"])

        drugs_found_percentage = (drugs_found_counts / total_drugs_found_counts) * 100

        drugs_handcuff_relation = (drugs_found_counts / total_handcuffed_counts) * 100
        
        total_drugs_found_percentage = (drugs_found_counts / race_counts) * 100

        
        race_list.append(race)
        handcuffed_percentage_list.append(handcuffed_percentage)
        handcuffed_count_list.append(handcuffed_counts)
        total_stops_count_list.append(race_counts)
        total_stops_percentage_list.append(total_stops_percentage)
        relative_handcuffed_percentage_list.append(relative_handcuffed_percentage)
        drugs_found_count_list.append(drugs_found_counts)
        drugs_found_percentage_list.append(drugs_found_percentage)
        drugs_handcuff_relation_list.append(drugs_handcuff_relation)
        total_drug_percentage_list.append(total_drugs_found_percentage)

        
    result_df = pd.DataFrame({
        "Race": race_list,
        "Number of Stops": total_stops_count_list,
        "Number of Handcuffs": handcuffed_count_list,
        "Instances of Drugs Found": drugs_found_count_list,
        "Proportion Total Stops %": total_stops_percentage_list,
        "Proportion Total Handcuffs %": handcuffed_percentage_list,
        "Proportion Drugs Found %": total_drug_percentage_list,
        "Relative Prop. of Handcuffs": relative_handcuffed_percentage_list,
        "Relative Prop. of Drugs Found": drugs_found_percentage_list
        
    })

    return result_df

general_stats(oakland_pd)
Out[156]:
Race Number of Stops Number of Handcuffs Instances of Drugs Found Proportion Total Stops % Proportion Total Handcuffs % Proportion Drugs Found % Relative Prop. of Handcuffs Relative Prop. of Drugs Found
0 White 3268 993 59 13.396188 10.328687 1.805386 30.385557 9.899329
1 Hispanic/Latino 6031 2391 149 24.722279 24.869981 2.470569 39.645167 25.000000
2 Black 12856 5635 359 52.699324 58.612440 2.792470 43.831674 60.234899
3 Asian 1173 303 18 4.808362 3.151654 1.534527 25.831202 3.020134
4 Multiracial 283 72 4 1.160074 0.748908 1.413428 25.441696 0.671141
5 Middle Eastern or South Asian 578 129 6 2.369338 1.341793 1.038062 22.318339 1.006711
6 Pacific Islander 177 79 0 0.725559 0.821718 0.000000 44.632768 0.000000
7 Native American 29 12 1 0.118877 0.124818 3.448276 41.379310 0.167785



What does this mean?

Black individuals constitute the racial group with the highest number of stops, handcuffs, and instances of drugs found. They have the highest proportion of total stops, handcuffs, and drug occurrences among all racial categories.

Hispanic/Latino individuals also have a significant number of stops, handcuffs, and instances of drugs found, with proportions relatively close to their total stops.

White individuals have a lower proportion of handcuffs and drug occurrences compared to their proportion of total stops.

The "Pacific Islander" and "Native American" groups have comparatively lower numbers of stops and handcuffs, but the proportion of handcuffs applied to them is relatively high compared to their total stops.





More Regions of Interest



Distribution of the time of the Stop by Race

In [ ]:
sns.displot(data=oakland_pd, x='Hour', hue='Race', kde=True, bins=24, palette='pastel', height=5, aspect=2)
plt.title("Distribution of Stop Hour by Race")
plt.xlabel("Hour of Stop")
plt.ylabel("Number of Stops")
plt.show()



By Race and Sex

In [ ]:
sns.displot(data=by_sex[by_sex["Gender"] != "Nonconforming/ T Male/ T Female"], x='Hour', hue='Race', col='Gender', kde=True, bins=24, palette='pastel', height=5, aspect=1)
plt.suptitle("Distribution of Stop Hour by Race and Gender", y=1.05)
plt.xlabel("Hour of Stop")
plt.ylabel("Number of Stops")
plt.show()



Distribution of Date of the Stop by Race

In [ ]:
sns.displot(data=oakland_pd, x='Day', hue='Race', kde=True, bins=36, palette='pastel', height=5, aspect=2)
plt.title("Distribution of Stop Day by Race")
plt.xlabel("Date of Stop")
plt.ylabel("Number of Stops")
plt.show()

• Interesting spike in the middle. Let's see what it means.



Days with the most stops by Race

In [150]:
def day_most_stops_by_race(dataframe):
    unique_races = dataframe['Race'].unique()

    for race in unique_races:
        race_stops = dataframe[dataframe['Race'] == race]
        stops_by_day_race = race_stops['Day'].value_counts()
        most_stops_day_race = stops_by_day_race.idxmax()
        most_stops_day_formatted = most_stops_day_race.strftime("%B %d")
        
        print(f"The day when race '{race}' had the most stops:", most_stops_day_formatted)

day_most_stops_by_race(oakland_pd)

The day when race 'White' had the most stops: June 07
The day when race 'Hispanic/Latino' had the most stops: July 04
The day when race 'Black' had the most stops: July 04
The day when race 'Asian' had the most stops: July 22
The day when race 'Multiracial' had the most stops: July 22
The day when race 'Middle Eastern or South Asian' had the most stops: April 14
The day when race 'Pacific Islander' had the most stops: July 25
The day when race 'Native American' had the most stops: November 15

Feeling Patriotic?

• I would be interested in knowing more about July 4th


• This is the day when most Black and Hispanic individuals are detained



Stop duration by Race

In [151]:
oakland_pd["Duration"].describe()
Out[151]:
count    24395.000000
mean        31.992949
std         42.382828
min          1.000000
25%         10.000000
50%         20.000000
75%         40.000000
max        999.000000
Name: Duration, dtype: float64



Limiting to 5 Hours (for better visualization)

In [152]:
duration_df = by_sex[by_sex['Duration'] <= 300]
In [153]:
sns.displot(data=duration_df[duration_df["Gender"] != "Nonconforming/ T Male/ T Female"], x='Duration', hue='Race', col="Gender", bins=15, kde=True, palette='pastel')
plt.title("Distribution of Stop Duration by Race")
plt.xlabel("Duration of Stop (minutes)")
plt.ylabel("Number of Stops")
plt.tight_layout()
plt.show()



Percentages of Stop Duration by Race)

In [154]:
percentage_by_race = oakland_pd.groupby(['Race', pd.cut(duration_df['Duration'], bins=[0, 30, 60, 90, 120, 150, 180, 210, 240, 270, 300])]).size().unstack()
percentage_by_race = percentage_by_race.div(percentage_by_race.sum(axis=1), axis=0) * 100
percentage_by_race = percentage_by_race.round(2)

percentage_by_race
Out[154]:
Duration (0, 30] (30, 60] (60, 90] (90, 120] (120, 150] (150, 180] (180, 210] (210, 240] (240, 270] (270, 300]
Race
Asian 81.30 13.49 2.22 1.62 0.09 1.02 0.00 0.17 0.00 0.09
Black 72.10 20.11 3.03 3.05 0.26 0.80 0.09 0.34 0.02 0.22
Hispanic/Latino 73.98 18.14 3.46 2.77 0.28 0.60 0.13 0.45 0.02 0.17
Middle Eastern or South Asian 79.41 15.22 1.04 2.42 0.00 1.04 0.35 0.35 0.17 0.00
Multiracial 87.63 9.89 1.06 1.06 0.00 0.35 0.00 0.00 0.00 0.00
Native American 68.97 20.69 6.90 3.45 0.00 0.00 0.00 0.00 0.00 0.00
Pacific Islander 71.19 14.69 5.08 8.47 0.00 0.56 0.00 0.00 0.00 0.00
White 76.87 17.57 2.95 1.60 0.31 0.37 0.09 0.18 0.00 0.06

• Running out of time, but an anova test would be interesting... Now let's write the report